Journal of Optimization Theory and Applications
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Learning convex combinations of continuously parameterized basic kernels
COLT'05 Proceedings of the 18th annual conference on Learning Theory
The Journal of Machine Learning Research
Discriminant kernel and regularization parameter learning via semidefinite programming
Proceedings of the 24th international conference on Machine learning
Nonlinear adaptive distance metric learning for clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Regularization Paths for ν-SVM and ν-SVR
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
Classification with Gaussians and Convex Loss
The Journal of Machine Learning Research
Analysis of the distance between two classes for tuning SVM hyperparameters
IEEE Transactions on Neural Networks
Representation of a fisher criterion function in a kernel feature space
IEEE Transactions on Neural Networks
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Learning Translation Invariant Kernels for Classification
The Journal of Machine Learning Research
On numerical optimization theory of infinite kernel learning
Journal of Global Optimization
Solving structured sparsity regularization with proximal methods
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Pattern Recognition
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Forecasting foreign exchange rates using kernel methods
Expert Systems with Applications: An International Journal
Algorithms for learning kernels based on centered alignment
The Journal of Machine Learning Research
Online Multiple Kernel Classification
Machine Learning
Learning with infinitely many features
Machine Learning
Regularized bundle methods for convex and non-convex risks
The Journal of Machine Learning Research
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We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels for one which minimizes a convex regularization functional. A unique feature of this approach compared to others in the literature is that the number of basic kernels can be infinite. We only require that they are continuously parameterized. For example, the basic kernels could be isotropic Gaussians with variance in a prescribed interval or even Gaussians parameterized by multiple continuous parameters. Our work builds upon a formulation involving a minimax optimization problem and a recently proposed greedy algorithm for learning the kernel. Although this optimization problem is not convex, it belongs to the larger class of DC (difference of convex functions) programs. Therefore, we apply recent results from DC optimization theory to create a new algorithm for learning the kernel. Our experimental results on benchmark data sets show that this algorithm outperforms a previously proposed method.